Course3Week3Lesson1a.ipynb

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  "metadata": {
    "colab": {
      "name": "Course 3 - Week 3 - Lesson 1a.ipynb",
      "provenance": [],
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      "toc_visible": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
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      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMgmjt7DcCNt",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rFiCyWQ-NC5D"
      },
      "source": [
        "# Single Layer LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Y20Lud2ZMBhW",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uAU8g7C0MPZE",
        "colab": {}
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AW-4Vo4TMUHb",
        "colab": {}
      },
      "source": [
        "# Get the data\n",
        "dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True, as_supervised=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVfhKpHsPOxq",
        "colab": {}
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ffvRUI0_McDS",
        "colab": {}
      },
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_dataset = train_dataset.shuffle(BUFFER_SIZE)\n",
        "train_dataset = train_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(train_dataset))\n",
        "test_dataset = test_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(test_dataset))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FxQooMEkMgur",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, 64),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QKI5dfPgMioL",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Uip7QOVzMoMq",
        "colab": {}
      },
      "source": [
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7mlgzaRDMtF6",
        "colab": {}
      },
      "source": [
        "NUM_EPOCHS = 10\n",
        "history = model.fit(train_dataset, epochs=NUM_EPOCHS, validation_data=test_dataset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Mp1Z7P9pYRSK",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "R_sX6ilIM515",
        "colab": {}
      },
      "source": [
        "plot_graphs(history, 'accuracy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RFEXtKtqNARB",
        "colab": {}
      },
      "source": [
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}